In the era of digital economy, the rural revitalization strategy has an urgent demand for composite talents, but the existing talent training mode is difficult to accurately match the industrial demand. The traditional cultivation method lacks in-depth analysis of the multidimensional characteristics of talents, and the construction of interdisciplinary cultivation platform is lagging behind. This study builds an interdisciplinary platform for rural revitalization talent cultivation based on big data analysis and multiple intelligence theory. TF-IDF algorithm and Kmeans clustering analysis are used to data mine 30 job samples from five major recruitment platforms to establish a talent portrait model with a two-dimensional multi-level labeling system. Through genetic optimization FCM algorithm for clustering analysis, the rural revitalization talents are classified into three types of prototypes: professional and technical, operation and management, and local return type. Develop an interdisciplinary digital learning platform based on ASP.NET Core framework to realize the functions of talent demand display, skill learning and data visualization. Taking 345 students of Ningbo Future Country College as the survey object, a five-level Likert scale was used to assess the effect of the platform. The results show that the questionnaire reliability coefficient of 0.884 and the KMO value of 0.904 meet high standards, 46.96% of the students are satisfied with the teacher interaction, 43.48% think that the course content is streamlined and efficient, and 40.87% say that they can absorb more than 75% of the lecture content. The study provides a theoretical basis and practical path for the precise cultivation of rural revitalization talents.
